Found 2024 packages in 0.01 seconds
Spatial Bayesian Factor Analysis
Implements a spatial Bayesian non-parametric factor analysis model
with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC).
Spatial correlation is introduced in the columns of the factor loadings
matrix using a Bayesian non-parametric prior, the probit stick-breaking
process. Areal spatial data is modeled using a conditional autoregressive
(CAR) prior and point-referenced spatial data is treated using a Gaussian
process. The response variable can be modeled as Gaussian, probit, Tobit, or
Binomial (using Polya-Gamma augmentation). Temporal correlation is
introduced for the latent factors through a hierarchical structure and can
be specified as exponential or first-order autoregressive. Full details of
the package can be found in the accompanying vignette. Furthermore, the
details of the package can be found in "Bayesian Non-Parametric Factor
Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019),
Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022)
Bayesian Tensor Factorization
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Bayesian Inference for Factor Modeling
Collection of procedures to perform Bayesian analysis on a variety
of factor models. Currently, it includes: "Bayesian Exploratory Factor
Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman,
R. Piatek (2014)
Bayesian Infinite Factor Models
Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419391/>. Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01.
A Bayesian Semiparametric Factor Analysis Model for Subtype Identification (Clustering)
Gene expression profiles are commonly utilized to infer disease subtypes and many clustering methods can be adopted for this task. However, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering. To deal with these challenges, we develop a novel clustering method in the Bayesian setting. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering.
Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior
distributions and Bayesian models. It includes point-estimates such as
Maximum A Posteriori (MAP), measures of dispersion (Highest Density
Interval - HDI; Kruschke, 2015
Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.
Bayesian Model Selection with Suspected Latent Grouping Factors
Implements the Bayesian model selection method with suspected latent
grouping factor methodology of Metzger and Franck (2020),
Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving